Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease

Abbas Saad Alatrany; Abir Hussain; Saad S.J. Alatrany; Jamila Mustafina; Dhiya Al-Jumeily

doi:10.1109/DeSE58274.2023.10099655

Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease

Abbas Saad Alatrany
, Abir Hussain
, Saad S.J. Alatrany
, Jamila Mustafina
, Dhiya Al-Jumeily

University of Sharjah

Liverpool John Moores University
University of Information Technology and Communications
Imam Ja'afar Al-Sadiq University
Kazan Volga Region Federal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

Original language	English
Title of host publication	DeSE 2023 - Proceedings
Subtitle of host publication	15th International Conference on Developments in eSystems Engineering
Editors	Dhiya Al-Jumeily, Header Abed Dhahad, Manj Jayabalan, Jade Hind, Jamila Mustafina, Sulaf Assi, Abir Hussain, Hissam Tawfik
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	60-64
Number of pages	5
ISBN (Electronic)	9798350335149
DOIs	https://doi.org/10.1109/DeSE58274.2023.10099655
State	Published - 2023
Event	15th International Conference on Developments in eSystems Engineering, DeSE 2023 - Baghdad, Iraq Duration: 9 Jan 2023 → 12 Jan 2023

Publication series

Name	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
Volume	2023-January
ISSN (Print)	2161-1343

Conference

Conference	15th International Conference on Developments in eSystems Engineering, DeSE 2023
Country/Territory	Iraq
City	Baghdad
Period	9/01/23 → 12/01/23

Keywords

ADNI
Alzheimer's disease
Early Prediction
LOAD
Machine Learning

Access to Document

10.1109/DeSE58274.2023.10099655

Cite this

Alatrany, A. S., Hussain, A., Alatrany, S. S. J., Mustafina, J., & Al-Jumeily, D. (2023). Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease. In D. Al-Jumeily, H. A. Dhahad, M. Jayabalan, J. Hind, J. Mustafina, S. Assi, A. Hussain, & H. Tawfik (Eds.), DeSE 2023 - Proceedings: 15th International Conference on Developments in eSystems Engineering (pp. 60-64). (Proceedings - International Conference on Developments in eSystems Engineering, DeSE; Vol. 2023-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DeSE58274.2023.10099655

Alatrany, Abbas Saad ; Hussain, Abir ; Alatrany, Saad S.J. et al. / Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease. DeSE 2023 - Proceedings: 15th International Conference on Developments in eSystems Engineering. editor / Dhiya Al-Jumeily ; Header Abed Dhahad ; Manj Jayabalan ; Jade Hind ; Jamila Mustafina ; Sulaf Assi ; Abir Hussain ; Hissam Tawfik. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 60-64 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

@inproceedings{6f5f4dde39e3420b9ee32626abccc132,

title = "Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease",

abstract = "Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.",

keywords = "ADNI, Alzheimer's disease, Early Prediction, LOAD, Machine Learning",

author = "Alatrany, \{Abbas Saad\} and Abir Hussain and Alatrany, \{Saad S.J.\} and Jamila Mustafina and Dhiya Al-Jumeily",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 15th International Conference on Developments in eSystems Engineering, DeSE 2023 ; Conference date: 09-01-2023 Through 12-01-2023",

year = "2023",

doi = "10.1109/DeSE58274.2023.10099655",

language = "English",

series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "60--64",

editor = "Dhiya Al-Jumeily and Dhahad, \{Header Abed\} and Manj Jayabalan and Jade Hind and Jamila Mustafina and Sulaf Assi and Abir Hussain and Hissam Tawfik",

booktitle = "DeSE 2023 - Proceedings",

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}

Alatrany, AS, Hussain, A, Alatrany, SSJ, Mustafina, J & Al-Jumeily, D 2023, Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease. in D Al-Jumeily, HA Dhahad, M Jayabalan, J Hind, J Mustafina, S Assi, A Hussain & H Tawfik (eds), DeSE 2023 - Proceedings: 15th International Conference on Developments in eSystems Engineering. Proceedings - International Conference on Developments in eSystems Engineering, DeSE, vol. 2023-January, Institute of Electrical and Electronics Engineers Inc., pp. 60-64, 15th International Conference on Developments in eSystems Engineering, DeSE 2023, Baghdad, Iraq, 9/01/23. https://doi.org/10.1109/DeSE58274.2023.10099655

Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease. / Alatrany, Abbas Saad; Hussain, Abir; Alatrany, Saad S.J. et al.
DeSE 2023 - Proceedings: 15th International Conference on Developments in eSystems Engineering. ed. / Dhiya Al-Jumeily; Header Abed Dhahad; Manj Jayabalan; Jade Hind; Jamila Mustafina; Sulaf Assi; Abir Hussain; Hissam Tawfik. Institute of Electrical and Electronics Engineers Inc., 2023. p. 60-64 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE; Vol. 2023-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease

AU - Alatrany, Abbas Saad

AU - Hussain, Abir

AU - Alatrany, Saad S.J.

AU - Mustafina, Jamila

AU - Al-Jumeily, Dhiya

PY - 2023

Y1 - 2023

N2 - Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

AB - Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

KW - ADNI

KW - Alzheimer's disease

KW - Early Prediction

KW - LOAD

KW - Machine Learning

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U2 - 10.1109/DeSE58274.2023.10099655

DO - 10.1109/DeSE58274.2023.10099655

M3 - Conference contribution

AN - SCOPUS:85158048986

T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE

SP - 60

EP - 64

BT - DeSE 2023 - Proceedings

A2 - Al-Jumeily, Dhiya

A2 - Dhahad, Header Abed

A2 - Jayabalan, Manj

A2 - Hind, Jade

A2 - Mustafina, Jamila

A2 - Assi, Sulaf

A2 - Hussain, Abir

A2 - Tawfik, Hissam

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on Developments in eSystems Engineering, DeSE 2023

Y2 - 9 January 2023 through 12 January 2023

ER -

Alatrany AS, Hussain A, Alatrany SSJ, Mustafina J, Al-Jumeily D. Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease. In Al-Jumeily D, Dhahad HA, Jayabalan M, Hind J, Mustafina J, Assi S, Hussain A, Tawfik H, editors, DeSE 2023 - Proceedings: 15th International Conference on Developments in eSystems Engineering. Institute of Electrical and Electronics Engineers Inc. 2023. p. 60-64. (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). doi: 10.1109/DeSE58274.2023.10099655

Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease

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